The fingerprint is one of the most widely used physical characteristics in biometric identification and verification. Recently, fingerprint identification has become increasingly popular as a biometric technology, since it has a good balance of desirable properties in terms of performance, reliability, etc. Thus, in recent years, there has been a great deal of research and development in the field of automatic fingerprint identification systems (AFIS), systems that automatically match one or many unknown fingerprints against a database of known fingerprints. One stage of AFIS is the fingerprint matching stage, where input fingerprint data is matched with one or more template fingerprints.
Among fingerprint matching algorithms, the minutia-based approach is one of the most widely used and reliable methods. Minutiae are small details of a fingerprint, including endings and bifurcations of fingerprint ridges. FIG. 1A shows a scanned fingerprint image with minutiae, where black lines indicate fingerprint ridges. The fingerprint includes ridge endings 102 and ridge bifurcations 104. FIGS. 1B and 1C show more detailed views of ridge endings and ridge bifurcations, respectively.
Referring to FIGS. 1B and 1C, each minutia can be represented by its location (indicated by round heads 106) and orientation (indicated by line segments 108). To be compatible with the ANSI-NIST standard, the minutia based approach must use only the location and orientation information of the minutiae—such an approach also allows for utilizing already-extracted minutiae templates of other systems, while being suitable for systems with limited computing resources. When only the location and orientation information of minutiae is used, fingerprint matching can be regarded as a point pattern matching problem of finding corresponding point pairs. However, there are at least two problems with this approach. First, the matching accuracy is influenced by spurious or dropped minutia from the minutiae extraction stage resulting from any change to skin condition. The other problem is nonlinear distortions in fingerprint images, for example, due to the elasticity of the skin and/or the pressure and movement of the finger during fingerprint acquisition (on the sensor surface).
To solve the above problems, various methods have been suggested, and recently, Kwon et al. proposed a fingerprint matching scheme using minutiae clustering and warping (“Fingerprint Matching Method Using Minutiae Clustering and Warping,” Proc. Int'l Conf. Pattern Recognition, volume 4, pages 525-528, 2006). The Kwon et al. method employs local neighborhood structures and the Thin-Plate Spline (TPS) model for the fingerprint matching.
However, as the TPS model basically is a surface interpolation scheme using the basis function having infinite responses, outliers or wrong correspondences between the minutiae would distort the fingerprint surface erroneously, resulting in global influences. This is the fundamental and common problem when the TPS model is used for fingerprint matching.